Emphasizing the value of business analytics, Diebold Nixdorf takes a proactive approach to optimizing the customer experience

Diebold Nixdorf

Exploratory data analysis and modeling in JMP® help analysts better understand customer feedback, home in on process issues and identify opportunities for new customer-centric solutions.

Results

With JMP, Diebold Nixdorf’s business analysts quickly identify key correlations in process data that, when acted upon by the company’s line-of-business managers, bring quantifiable value through stronger customer relationships. In one case, analysts were able in a matter of minutes to identify an easily fixed invoicing problem as the root cause of the company’s low Net Promoter Score.

Some companies rely on data analytics to improve their products. Others use data analysis and reporting to optimize processes. Ultimately, these data-driven efforts benefit customers – through new innovations, better reliability and lower prices. Financial and retail technology giant Diebold Nixdorf is no exception. A company that measures success largely in customer-centric terms, Diebold Nixdorf is embracing the power and potential of state-of-the-art business analytics to drive higher customer satisfaction and stronger long-term customer relationships.

Positive experiences at every touchpoint

Diebold Nixdorf manufactures, installs and services financial technology such as ATMs, point-of-sale terminals and related hardware and software for the financial, retail and commercial markets. In fact, it claims more than one-third of the global ATM market. Based in Canton, OH, with 23,000 employees serving customers in 130 countries, the company generates annual revenues of $4.6 billion. Like any B2B enterprise, Diebold Nixdorf delivers customer satisfaction not just through the quality of its products and services. It also needs to achieve positive customer experiences at every touchpoint – including statements of work, contracts and invoices.

“We don’t just deliver the equipment and installation services,” says Tyler Wise, Project Analyst with Diebold Nixdorf’s Global Business Services group. “We also deliver invoicing and information about services performed. We need to make that process as painless as possible so that customers don’t dispute invoices, pay promptly and have an excellent experience.”

That’s where analytics comes in. Wise and his colleagues use statistical methods to explore the data the company collects, helping Diebold Nixdorf to understand customer sentiment, identify potential process issues and find solutions that will optimize customer satisfaction. “We deal with a large volume of customers,” Wise notes. “If even 1 percent of accounts had an issue, that would be a large number. But keeping track of all the data and processes around those customers can be difficult.”

In fact, Diebold Nixdorf’s data streams are extremely large. Sifting through an ocean of information would be impossibly time-consuming – and in some cases simply impossible – using manual processes or less sophisticated tools like Microsoft Excel. “When I first walked in the door at Diebold Nixdorf,” Wise recalls, “they (were measuring and running a number of) analyses with Excel, but to go to that next level, they needed a more powerful tool – something that lets you say, ‘OK, we're measuring all these things that have happened in the past and we're seeing how we're getting better or what's being missed.’ JMP is what has helped us be able to see the big picture more clearly and in real time.”

Wise first became familiar with JMP®, a statistical discovery software package from SAS, while working toward an MBA at Kent State University’s College of Business Administration. In a program largely focused on producing analytics-savvy graduates, Wise says the software helped students navigate new statistical concepts – and see how data can inform operational improvements. “I was intrigued by how analytics, models, project management, process – they all share a similar nature. Analytics isn’t just about number crunching. You can use analytics on a daily basis to drive project and team management and performance. And with JMP, you can learn to use the tool and make decisions right away.”

It was this philosophy that, post-Kent State, Wise brought with him to Diebold Nixdorf where a nascent culture of analytics, fostered by division management, was slowly transforming the business. “Our vice president says that if you can’t make an argument based on data, then it’s probably based on feeling,” Wise relates. When making process improvements, Wise says, “Someone will say, ‘I think this is the problem.’ Then the process owner will say, ‘Do you have proof?’ If you don’t have data, that conversation isn’t going to go very far.”

“That’s why our focus is on measuring process performance and customer satisfaction. If we can’t measure what we’re doing, then we can’t know how well we’re doing it and whether there are opportunities for improvements.”

A decision tree in JMP® provides insight in minutes

Once at Diebold Nixdorf, it didn’t take long for Wise to put his analytical skills to the test on a tangible problem: Like many enterprises, Diebold Nixdorf uses the Net Promoter Score (NPS) to measure customer loyalty. And the company had just received a score that was well under what they had expected. The division’s VP called a meeting to find out why – and ask the team what could be done to improve.

Wise called up JMP and used a decision tree to explore the correlation between the NPS results and certain customer characteristics. “With just the first few splits of the tree, we could see that the data split between good and bad scores at ‘no disputes on invoicing’ and ‘disputes on invoicing,’” Wise recalls. In essence, customer satisfaction overwhelmingly depended on whether they had experienced an invoicing problem. “You see that come up in the model and you start talking through it, and you think, ‘oh yeah, that makes sense. Of course that's what the problem is.’

“It sounds simple, but only because JMP made it simple. JMP gave us the answer literally in seconds … So instead of spending all that time trying to find the why or the what, we dove right into what was [driving the problem] and what we could do to fix it.” Instead of spending time and effort looking for the cause, Diebold Nixdorf could focus on the solution.

From experience to relationships

“No process can be 100 percent error-free,” Wise concedes. “But the more errors we can catch before they go out the door, the greater the benefit to our customers.” And where JMP helps those customers the most is in what they don’t experience, Wise says. “If a customer gets a statement of work or an invoice that has inaccuracies, they have to call us, and that’s a pain point. But if everything we provide the customer lines up, there’s no friction, and the customer has a better experience.”

To that end, Diebold Nixdorf is using JMP to use process history to proactively improve future performance. “We have parameters that say, when these events take place, the customer gives us a poor NPS rating,” Wise explains. “Now, if we see a similar issue, even before we get customer feedback, we know to address it proactively.” If an account is disputing invoices, say, Diebold Nixdorf can reach out to the customer to determine if there’s confusion about the process or if documentation isn’t being delivered promptly.

A more informed picture of performance sheds light on how business processes affect customers

Wise says that exploratory analysis gives Diebold Nixdorf a clear picture of how it is performing – and how process changes affect customers’ experience – all based on data the company is already collecting. With that kind of insight, Diebold Nixdorf can be more strategic in its operations. For example, “we can take historic data and build a model that predicts, based on past performance, how things will trend in the future,” Wise says. “What’s really valuable about JMP is that you can see the inputs that are driving to a conclusion.”

Ultimately, it’s Diebold Nixdorf’s process experts and line-of-business managers who use those insights to make decisions. That’s where JMP outputs bring real, quantifiable value. “You need to present data in a way managers can understand,” Wise emphasizes. “With JMP, you can say, ‘You don’t need to look at an Excel file with 100,000 entries. Instead, look at this graph in JMP. Now what is that visualization telling you?’”

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The results illustrated in this article are specific to the particular situations, business models, data input and computing environments described herein. Each SAS customer’s experience is unique, based on business and technical variables, and all statements must be considered nontypical. Actual savings, results and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software.